Face morphing attacks create face images that are verifiable to multiple identities. Associating such images to identity documents lead to building faulty identity links, causing attacks on operations like border crossing. Most of previously proposed morphing attack detection approaches directly classified features extracted from the investigated image. We discuss the operational opportunity of having a live face probe to support the morphing detection decision and propose a detection approach that take advantage of that. Our proposed solution considers the facial landmarks shifting patterns between reference and probe images. This is represented by the directed distances to avoid confusion with shifts caused by other variations. We validated our approach using a publicly available database, built on 549 identities. Our proposed detection concept is tested with three landmark detectors and proved to outperform the baseline concept based on handcrafted and transferable CNN features.

Face images processed by a biometric system are expected to be used for recognition purposes only. However, recent work presented possibilities for automatically deducing additional information about an individual from their face data. By using soft-biometric estimators, information about gender, age, ethnicity, sexual orientation or the health state of a person can be obtained. This raises a major privacy issue. Previous works presented supervised solutions that require large amount of private data in order to suppress a single attribute. In this work, we propose a privacy-preserving solution that does not require these sensitive information and thus, works in an unsupervised manner. Further, our approach offers privacy protection that is not limited to a single known binary attribute or classifier. We do that by proposing similarity-sensitive noise transformations and investigate their effect and the effect of dimensionality reduction methods on the task of privacy preservation. Experiments are done on a publicly available database and contain analyses of the recognition performance, as well as investigations of the estimation performance of the binary attribute of gender and the continuous attribute of age. We further investigated the estimation performance of these attributes when the prior knowledge about the used privacy mechanism is explicitly utilized. The results show that using this information leads to significantly enhancement of the estimation quality. Finally, we proposed a metric to evaluate the trade-off between the privacy gain and the recognition loss for privacy-preservation techniques. Our experiments showed that the proposed cosine-sensitive noise transformation was successful in reducing the possibility of estimating the soft private information in the data, while having significantly smaller effect on the intended recognition performance.

Once upon a time, there was a blacklisted criminal who usually avoided appearing in public. He was surfing the Web, when he noticed, what had to be a targeted advertisement announcing a concert of his favorite band. The concert was in a near town, and the only way to get there was by train. He was worried, because he heard in the news about the new face identification system installed at the train station. From his last stay with the police, he remembers that they took these special face images with the white background. He thought about what can he do to avoid being identified and an idea popped in his mind “what if I can make a crazy-face, as the kids call it, to make my face look different? What do I exactly have to do? And will it work?”. He called his childhood geeky friend and asked him if he can build him a face recognition application he can tinker with. The geeky friend was always interested in such small projects where he can use open-source resources and didn’t really care about the goal, as usual. The criminal tested the application and played around, trying to figure out how can he make a crazy-face that won’t be identified as himself. On the day of the concert, he took off to the train station with some doubt in his mind and fear in his soul. To know what happened next, you should read the rest of this paper.

Deep and Multi-algorithmic Gender Classification of Single Fingerprint Minutiae

Accurate fingerprint gender estimation can positively affect several applications, since fingerprints are one of the most widely deployed biometrics. For example, gender classification in criminal investigations may significantly minimize the list of potential subjects. Previous work mainly offered solutions for the task of gender classification based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications, including forensics and the fast growing field of consumer electronics. Moreover, partial fingerprints are not well-defined. Therefore, this work improves the gender decision performance on a well-defined partition of the fingerprint. It enhances gender estimation on the level of a single minutia. Working on this level, we propose three main contributions that were evaluated on a publicly available database. First, a convolutional neural network model is offered that outperformed baseline solutions based on hand crafted features. Second, several multi-algorithmic fusion approaches were tested by combining the outputs of different gender estimators that help further increase the classification accuracy. Third, we propose including minutia detection reliability in the fusion process, which leads to enhancing the total gender decision performance. The achieved gender classification performance of a single minutia is comparable to the accuracy that previous work reported on a quarter of aligned fingerprints including more than 25 minutiae.

Fingerprint and Iris Multi-biometric Data Indexing and Retrieval

Indexing of multi-biometric data is required to facilitatefast search in large-scale biometric systems. Previous worksaddressing this issue in multi-biometric databases focused onmulti-instance indexing, mainly iris data. Few works addressedthe indexing in multi-modal databases, with basic candidate listfusion solutions limited to joining face and fingerprint data. Irisand fingerprint are widely used in large-scale biometric systemswhere fast retrieval is a significant issue. This work proposes jointmulti-biometric retrieval solution based on fingerprint and irisdata. This solution is evaluated under eight different candidatelist fusion approaches with variable complexity on a databaseof 10,000 reference and probe records of irises and fingerprints.Our proposed multi-biometric retrieval of fingerprint and irisdata resulted in a reduction of the miss rate (1- hit rate) at 0.1%penetration rate by 93% compared to fingerprint indexing and88% compared to iris indexing.

Normalization is an important step for different fusion, classification, and decision making applications. Previous normalization approaches considered bringing values from different sources into a common range or distribution characteristics. In this work we propose a new normalization approach that transfers values into a normalized space where their relative performance in binary decision making is aligned across their whole range. Multi-biometric verification is a typical problem where information from different sources are normalized and fused to make a binary decision and therefore a good platform to evaluate the proposed normalization.We conducted an evaluation on two publicly available databases and showed that the normalization solution we are proposing consistently outperformed state-of-the-art and best practice approaches, e.g. by reducing the false rejection rate at 0.01% false acceptance rate by 60- 75% compared to the widely used z-score normalization under the sum-rule fusion.

What Can a Single Minutia Tell about Gender?

2018

2018 International Workshop on Biometrics and Forensics (IWBF)

Since fingerprints are one of the most widely deployed biometrics, several applications can benefit from an accurate fingerprint gender estimation. Previous work mainly tackled the task of gender estimation based on complete fingerprints. However, partial fingerprint captures are frequently occurring in many applications including forensics and consumer electronics, with the considered ratio of the fingerprint is variable. Therefore, this work investigates gender estimation on a small, detectable, and well-defined partition of a fingerprint. It investigates gender estimation on the level of a single minutia. Working on this level, we propose a feature extraction process that is able to deal with the rotation and translation invariance problems of fingerprints. This is evaluated on a publicly available database and with five different binary classifiers. As a result, the information of a single minutia achieves a comparable accuracy on the gender classification task as previous work using quarters of aligned fingerprints with an average of more than 25 minutiae.

Efficient, Accurate, and Rotation-Invariant Iris Code

2017

IEEE Signal Processing Letters

The large scale of the recently demanded biometric systems has put a pressure on creating a more efficient, accurate, and private biometric solutions. Iris biometrics is one of the most distinctive and widely used biometric characteristics. High-performing iris representations suffer from the curse of rotation inconsistency. This is usually solved by assuming a range of rotational errors and performing a number of comparisons over this range, which results in a high computational effort and limits indexing and template protection. This work presents a generic and parameter-free transformation of binary iris representation into a rotation-invariant space. The goal is to perform accurate and efficient comparison and enable further indexing and template protection deployment. The proposed approach was tested on a database of 10 000 subjects of the ISYN1 iris database generated by CASIA. Besides providing a compact and rotational-invariant representation, the proposed approach reduced the equal error rate by more than 55% and the computational time by a factor of up to 44 compared to the original representation.

General Borda Count for Multi-biometric Retrieval

Indexing of multi-biometric data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by including biometric sources of different nature, utilizing the knowledge about the biometric sources, and optimizing and tuning the retrieval performance. This work presents a generalized multi-biometric retrieval approach that adapts the Borda count algorithm within an optimizable structure. The approach was tested on a database of 10k reference and probe instances of the left and the right irises. The experiments and comparisons to five baseline solutions proved to achieve advances in terms of general indexing performance, tunability to certain operating points, and response to missing data. A clear advantage of the proposed solution was noticed when faced by candidate lists of low quality.

Biometrics is a rapidly developing field of research and biometric-based identification systems experience a massive growth all around the world caused by the gaining industrial, government and citizen acceptance. The US-VISIT program uses biometric systems to enforce homeland and border security, whereas in the United Arab Emirates (UAE), biometric systems play a major role in the border control process. Similar, in India, biometrics have gained a great deal of attention, as the Unique Identification Authority of India (UIDAI) have already registered over one billion Indian citizens in the last 7 years (uidai.gov.in). Despite the rapid propagation of large-scale databases, the majority of researchers are still focusing on the matching accuracy of small databases, while neglecting scalability and speed issues. Identity association is usually determined by comparing input data against every entry in the database, which causes computational problems when it comes to large-scale databases. Biometric indexing aims to reduce the number of candidate identities to be considered by an identification system when searching for a match in large biometric databases. However, this is a challenging task since biometric data is fuzzy and does not exhibit any natural sorting order. Current indexing methods are mainly based on tree traversal (using kd-trees, B-trees, R-trees) which suffer from the curse of dimensionality, while other indexing methods are based on hashing, which suffer from pure key generation. The goal of this thesis is to develop an indexing scheme based on multiple biometric modalities. It aims to present the main results of research focusing on iris and fingerprint indexing. Fingerprints are undisputedly the most studied biometric modality that are extensive used in civil and forensic recognition systems. Together with the potential rise of iris recognition accurateness along with enhanced robustness, indexing of this modalities becomes a promising field of research. Different unimodal and multimodal identification approaches have already been proposed in past years. However, most of them trade fast identification rates at the cost of accuracy, while the remaining make use of complex indexing structures, which results in a complete restructuring if insertions or deletions are necessary. This work offers a framework for fast and accurate iris indexing as well as effective indexing schemes to combine multiple modalities. To achieve that, three main contributions are made: First, a new rotation invariant iris representation was developed, reducing the equal error rate by more than 55% and the computation time by a factor up to 44 compared to the original representation. Second, this representation was used to construct an indexing scheme, which reaches a hit rate of 99.7% at 0.1% penetration rate, outperforming state of the art algorithms. And third, a general rank-level indexing fusion scheme was developed to effectively combine multiple sources, achieving over 99.98% hit rate at same penetration rate of 0.1%.

Indexing of Single and Multi-instance Iris Data Based on LSH-Forest and Rotation Invariant Representation

Indexing of iris data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by the tradeoffs between accuracy, computational efficacy, storage costs, and maintainability. This work presents an iris indexing approach based on rotation invariant iris representation and LSH-Forest to produce an accurate and easily maintainable indexing structure. The complexity of insertion or deletion in the proposed method is limited to the same logarithmic complexity of a query and the required storage grows linearly with the database size. The proposed approach was extended into a multi-instance iris indexing scheme resulting in a clear performance improvement. Single iris indexing scored a hit rate of 99.7% at a 0.1% penetration rate while multi-instance indexing scored a 99.98% hit rate at the same penetration rate. The evaluation of the proposed approach was conducted on a large database of 50k references and 50k probes of the left and the right irises. The advantage of the proposed solution was put into prospective by comparing the achieved performance to the reported results in previous works.